Abstract
In order to improve the training effect of applied talents, quantitative evaluation and big data analysis are used to evaluate the training of applied talents. A quantitative analysis model of applied talents training based on big data and probability statistics is proposed. The statistical mathematical analysis model of applied talents training is constructed, and the significance of applied talents training is analyzed by using T statistical test analysis method, and the benefit distribution model of applied talents training is established. The cumulative average analysis method is used to evaluate the bilateral reliability of applied talents training, and the big data mining and feature extraction methods are used to analyze the characteristics of applied talents training. The big data robust mining model for the cultivation of applied talents is constructed, and the descriptive statistical analysis method of single variable is taken, the statistical analysis of probability theory and big data analysis method are used to realize the evaluation of benefit index for the cultivation of applied talents. The results of empirical analysis show that the model has good accuracy and high level of confidence in the quantitative evaluation of applied talents training.
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References
Shi, E., Li, Q., Gu, D., Zhao, Z.: Weather radar echo extrapolation method based on convolutional neural networks. J. Comput. Appl. 38(3), 661–665 (2018)
Fletcher, T.D., Andrieu, H., Hamel, P.: Understanding, management and modelling of urban hydrology and its consequences for receiving waters, a state of the art. Adv. Water Resour. 51(1), 261–279 (2013)
Ma, B., Xie, X.: PSHO-HF-PM: an efficient proactive spectrum handover mechanism in cognitive radio networks. Wirel. Pers. Commun. 79(3), 1–23 (2014)
Ji, Y., Li, Y., Shi, C.: Aspect rating prediction based on heterogeneous network and topic model. J. Comput. Appl. 37(11), 3201–3206 (2017)
Xue, J., Ni, X.: On the reform of college english teaching under the trend of educational informatization. Integr. Inf. Technol. Teach. Pract. 45(12), 43–45 (2015)
Zheng, B., Gu, X.: Walk through the “cloud” end of practical education information–on the application of cloud platform in English teaching. In: Education and Teaching Forum, vol. 03, pp. 263–265 (2016)
Shen, W., Wynter, L.: A new one-level convex optimization approach for estimating origin–destination demand. Transp. Res. Part B: Methodol. 46(10), 1535–1555 (2012)
Rao, C.S., Reddy, K.C.K., Rao, D.S.: Power control technique for efficient call admission control in advanced wireless networks. Int. J. Comput. Sci. Eng. 4(6), 962–973 (2012)
Shi, H.-Y., Wang, W.-L., Kwok, N.M., et al.: Game theory for wireless sensor networks: a survey. Sensors 12(7), 9055–9097 (2012)
Zhang, G.-P., Liu, P., Ding, E.-J.: Energy efficient resource allocation in non-cooperative multi-cell OFDMA systems. J. Syst. Eng. Electron. 22(1), 175–182 (2011)
Xiong, X., Yang, L., Ma, Y., Zhuang, Z.: Alerting algorithm of low-level wind shear based on fuzzy C-means. J. Comput. Appl. 38(3), 655–660 (2018)
Zheng, J.F., Zhang, J., Zhu, K.Y., et al.: Gust front statistical characteristics and automatic identification algorithm for CINRAD. Acta Meteorologica Sinica 28(4), 607–623 (2014). https://doi.org/10.1007/s13351-014-3240-2
Hwang, Y., Yu, T.Y., Lakshmanan, V., et al.: Neuro-fuzzy gust front detection algorithm with S-band polarimetric radar. IEEE Trans. Geosci. Remote Sens. 55(3), 1618–1628 (2017)
Sun, H., Zhang, H., Wu, J.: Correlated scale-free network with community: modeling and transportation dynamics. Nonlinear Dyn. 69(4), 2097–2104 (2012)
Killip, R., Visan, M.: The defocusing energy-supercritical nonlinear wave equation in three space dimensions. Trans. Am. Math. Soc. 363(7), 3893–3934 (2011)
Sun, C.L., Qian, M.M.: The research on applied talenttraining mechanism of undergraduate based on the combination of professional programmatic accreditation and double certificates. In: International Computer Conference on Wavelet Active Media Technology and Information Processing, vol. 34, no. 8, 316–319 (2014)
Nuo, L.I.: The training scheme of applied talents on combination of enterprises and universities research in local universities. J. Jiaying Univ. 42(15), 321–327 (2011)
Yang, L.Y., Jiang, J.L.: Investigation and research on applied talents training of educational technology specialty in local undergraduate universities—with Jiaying University as example. J. Jiaying Univ. 41(9), 241–249 (2013)
Sun, L., Shen, Q., Zhao, L.: Construction of applied talents training mode for the statistics majors based on the Big Data technology. J. Jilin Inst. Chem. Technol. 34(6), 35–40 (2017)
Yu, Y.: Study on probability and mathematical statistics teaching in the training pattern of applied professionals. J. Langfang Teachers Univ. (Nat. Sci. Ed.) 16(4), 118–119 (2016)
Peng, T., Sun, L., Liu, C.: Research on applied IT talents cultivation based on the OBE model—big data approach. Softw. Eng. 20(8), 56–58 (2017)
Zhang, C., Yang, C., Wu, S., Zhang, X., Nie, W.: A straightforward direct traction boundary integral method for two-dimensional crack problems simulation of linear elastic materials. Comput. Mater. Continua 58(3), 761–775 (2019)
Yuan, F., Zhang, Q., Xia, X.: Effect of reinforcement corrosion sediment distribution characteristics on concrete damage behavior. Comput. Mater. Continua 58(3), 777–793 (2019)
Guo, F., et al.: Research on the law of garlic price based on big data. Comput. Mater. Continua 58(3), 795–808 (2019)
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Wu, L., Yang, J. (2020). Research on Application of Big Data Combined with Probability Statistics in Training Applied Talents. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12240. Springer, Cham. https://doi.org/10.1007/978-3-030-57881-7_59
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